79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
from __future__ import annotations
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from typing import List
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from dataclasses import dataclass
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from fuzzywuzzy import fuzz
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CONF_THRESHOLD = 60
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# word predictions with lower confidence will be filtered out
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@dataclass
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class PredictedWord:
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__slots__ = 'confidence', 'text'
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confidence: int
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text: str
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class PredictedFrame:
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index: int # 0-based index of the frame
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words: List[PredictedWord]
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confidence: int # total confidence of all words
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text: str
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def __init__(self, index, pred_data: str):
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self.index = index
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self.words = []
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block = 0 # keep track of line breaks
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for l in pred_data.splitlines()[1:]:
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word_data = l.split()
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if len(word_data) < 12:
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# no word is predicted
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continue
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_, _, block_num, *_, conf, text = word_data
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block_num, conf = int(block_num), int(conf)
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# handle line breaks
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if block < block_num:
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block = block_num
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self.words.append(PredictedWord(0, '\n'))
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if conf >= CONF_THRESHOLD:
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self.words.append(PredictedWord(conf, text))
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self.confidence = sum(word.confidence for word in self.words)
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self.text = ''.join(word.text + ' ' for word in self.words).strip()
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def is_similar_to(self, other: PredictedFrame, threshold=60) -> bool:
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if len(self.text) == 0 or len(other.text) == 0:
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return False
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return fuzz.ratio(self.text, other.text) >= threshold
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class PredictedSubtitle:
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frames: List[PredictedFrame]
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def __init__(self, frames: List[PredictedFrame]):
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self.frames = [f for f in frames if f.confidence > 0]
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@property
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def text(self) -> str:
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if self.frames:
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conf_max = max(f.confidence for f in self.frames)
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return next(f.text for f in self.frames if f.confidence == conf_max)
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return ''
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@property
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def index_start(self) -> int:
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if self.frames:
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return self.frames[0].index
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return 0
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@property
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def index_end(self) -> int:
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if self.frames:
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return self.frames[-1].index
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return 0
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